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README (3).md

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+---
+title: Hyper FLUX 8Steps LoRA
+emoji: 🖼
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.26.0
+app_file: app.py
+pinned: false
+license: openrail++
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (5).py

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+import spaces
+import argparse
+import os
+import time
+from os import path
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+
+cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
+os.environ["TRANSFORMERS_CACHE"] = cache_path
+os.environ["HF_HUB_CACHE"] = cache_path
+os.environ["HF_HOME"] = cache_path
+
+import gradio as gr
+import torch
+from diffusers import FluxPipeline
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+class timer:
+    def __init__(self, method_name="timed process"):
+        self.method = method_name
+    def __enter__(self):
+        self.start = time.time()
+        print(f"{self.method} starts")
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        end = time.time()
+        print(f"{self.method} took {str(round(end - self.start, 2))}s")
+
+if not path.exists(cache_path):
+    os.makedirs(cache_path, exist_ok=True)
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
+pipe.fuse_lora(lora_scale=0.125)
+pipe.to(device="cuda", dtype=torch.bfloat16)
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        <div style="text-align: center; max-width: 650px; margin: 0 auto;">
+            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Hyper-FLUX-8steps-LoRA</h1>
+            <p style="font-size: 1rem; margin-bottom: 1.5rem;">AutoML team from ByteDance</p>
+        </div>
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            with gr.Group():
+                prompt = gr.Textbox(
+                    label="Your Image Description",
+                    placeholder="E.g., A serene landscape with mountains and a lake at sunset",
+                    lines=3
+                )
+                
+                with gr.Accordion("Advanced Settings", open=False):
+                    with gr.Group():
+                        with gr.Row():
+                            height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
+                            width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
+                        
+                        with gr.Row():
+                            steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=8)
+                            scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
+                        
+                        seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
+                
+                generate_btn = gr.Button("Generate Image", variant="primary", scale=1)
+
+        with gr.Column(scale=4):
+            output = gr.Image(label="Your Generated Image")
+    
+    gr.Markdown(
+        """
+        <div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
+            <h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
+            <ol style="padding-left: 1.5rem;">
+                <li>Enter a detailed description of the image you want to create.</li>
+                <li>Adjust advanced settings if desired (tap to expand).</li>
+                <li>Tap "Generate Image" and wait for your creation!</li>
+            </ol>
+            <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
+        </div>
+        """
+    )
+
+    @spaces.GPU
+    def process_image(height, width, steps, scales, prompt, seed):
+        global pipe
+        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+            return pipe(
+                prompt=[prompt],
+                generator=torch.Generator().manual_seed(int(seed)),
+                num_inference_steps=int(steps),
+                guidance_scale=float(scales),
+                height=int(height),
+                width=int(width),
+                max_sequence_length=256
+            ).images[0]
+
+    generate_btn.click(
+        process_image,
+        inputs=[height, width, steps, scales, prompt, seed],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

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